One of the most significant dangers in space is the intense radiation environment. The Sun is radiating high-speed electrons, protons and other atomic fragments that can harm people and damage equipment.

The EU-funded project EHEROES (Environment for human exploration and robotic experimentation in space) has quantified the dangers of space exploration to both life and machine operations. Value-added data on solar radiation have been and are still being collected and made available via online databases and data services that provide users with supporting information and data mining tools.

Solar and space events and their evolution have been documented, particularly coronal mass ejections and solar energetic particle storms. The key physical processes targeted by the project are coronal heating, solar flares and eruptive regions, the 3D structure of solar magnetic fields above eruptive regions, and the variation of solar irradiance with time.

The project has provided provide a better understanding of the conditions of the space environment (sometimes called space weather) and its likely variability. Investigators documented how the most extreme solar events are expected to evolve in space and time as they propagate outwards. This will enhance the ability of mission planners to minimise and mitigate radiation hazards. Variability on timescales up to a solar activity cycle (about 11 years) have been estimated.

Special consideration was given to the most likely space missions to venture beyond low Earth orbit – to the Moon, Mars and beyond. The space environment around the moon and Mars is strongly affected by these bodies. For example, lunar magnetic anomalies (regions of unusually high magnetic fields) can affect the incident solar particles and fields.

The models developed by the project will be useful when considering how spacecraft may be charged as they pass through solar and planetary magnetic fields. They may also influence the payloads selected by future spacecraft, to secure the measurements found to be of most diagnostic value, so that model predictions can be further refined.